Whittington James C R, Bogacz Rafal
MRC Brain Network Dynamics Unit, University of Oxford, Oxford, OX1 3TH, U.K., and FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, U.K.
MRC Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, U.K., and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, U.K.
Neural Comput. 2017 May;29(5):1229-1262. doi: 10.1162/NECO_a_00949. Epub 2017 Mar 23.
To efficiently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error backpropagation algorithm. However, in this algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of presynaptic and postsynaptic neurons. Several models have been proposed that approximate the backpropagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. Furthermore, for certain parameters, the weight change in the predictive coding model converges to that of the backpropagation algorithm. This suggests that it is possible for cortical networks with simple Hebbian synaptic plasticity to implement efficient learning algorithms in which synapses in areas on multiple levels of hierarchy are modified to minimize the error on the output.
为了有效地从反馈中学习，皮层网络需要在皮层层次结构的多个层面上更新突触权重。一种计算突触权重此类变化的有效且著名的算法是误差反向传播算法。然而，在该算法中，突触权重的变化是与被修改突触没有直接连接的神经元的权重和活动的复杂函数，而生物突触的变化仅由突触前和突触后神经元的活动决定。已经提出了几种用局部突触可塑性近似反向传播算法的模型，但这些模型需要对网络进行复杂的外部控制或相对复杂的可塑性规则。在这里，我们表明，在预测编码框架中开发的网络可以仅使用简单的局部赫布可塑性完全自主地高效执行监督学习。此外，对于某些参数，预测编码模型中的权重变化收敛于反向传播算法的权重变化。这表明，具有简单赫布突触可塑性的皮层网络有可能实现高效的学习算法，其中层次结构多个层面区域中的突触被修改以最小化输出误差。
